Spotify Exploratory Dataset Analysis

Introduction

Code and Documentation

Version control:

• Git

• Github

• Consistent file structure and naming (e.g., 0-dataMunging)

Clear and readable plots

Clear and properly commented code

Background

• The data was aquired through Spotify API in 2020 by TidyTuesday

• The class of the data frame

[1] "spec_tbl_df" "tbl_df"      "tbl"         "data.frame" 

• The number of rows

[1] 32833

The data set

• The variables (columns)

 [1] "track_id"                 "track_name"              
 [3] "track_artist"             "track_popularity"        
 [5] "track_album_id"           "track_album_name"        
 [7] "track_album_release_date" "playlist_name"           
 [9] "playlist_id"              "playlist_genre"          
[11] "playlist_subgenre"        "danceability"            
[13] "energy"                   "key"                     
[15] "loudness"                 "mode"                    
[17] "speechiness"              "acousticness"            
[19] "instrumentalness"         "liveness"                
[21] "valence"                  "tempo"                   
[23] "duration_ms"             

The different Genres

• The main genres in the data

  edm latin   pop   r&b   rap  rock 
 6043  5155  5507  5431  5746  4951 

Cleaning: NA values

• Check for the NA observation

  # A tibble: 5 × 4
    track_name track_artist track_album_name track_id              
    <chr>      <chr>        <chr>            <chr>                 
  1 <NA>       <NA>         <NA>             69gRFGOWY9OMpFJgFol1u0
  2 <NA>       <NA>         <NA>             5cjecvX0CmC9gK0Laf5EMQ
  3 <NA>       <NA>         <NA>             5TTzhRSWQS4Yu8xTgAuq6D
  4 <NA>       <NA>         <NA>             3VKFip3OdAvv4OfNTgFWeQ
  5 <NA>       <NA>         <NA>             69gRFGOWY9OMpFJgFol1u0

• After investigating, these are unique songs

Cleaning: Duplicates

• Songs that have the same name

  [1] 9383

• Songs that have the same ID

  [1] 4477

Clean data

• The new data with no duplicates

  edm latin   pop   r&b   rap  rock 
 4877  4137  5132  4504  5401  4305 

Exploratory Data Analysis

Initial Data Exploration

• Most popular artists

Initial Data Exploration

• Most popular albums

Initial Data Exploration

• Most popular genres

Exploratory Data Analysis

How features change within genres

Latin

• Latin stands out in danceability and valence.

• danceability

Latin

• Valance

Release Date

• Is there a relationship between album release date and popularity?
• Are features affected by album release date?

Correlation

What’s the correlation between energy, loudness and acousticness?

• Positive correlation between energy and loudness

• Negative correlation between energy and acousticness

Correlation

• Does the correlation exist in a specific genre

Correlation

• Does the correlation exist in a specific genre

Track duration

Does the track duration affect the popularity of the song?

Conclusion

The analysis of the Spotify dataset yielded the following results:

• Pop and Latin are the top most popular genres.

• The higher the danceability/ valence, the more positively it correlates to the popularity.

• Energy and loudness are positively correlated.

• Energy and acoustics are negativelly correlated.

• Track duration does not have a clear effect on the song popularity.